Thursday, January 12, 2012

Channeling information and semantic convolution

OK, our generic problem is allowing system controllers to channel data via rules, even though the data is available as an unregulated semantic nest. Right, this is the basic problem that is causing CIOs to spend million on regulating a few hundred lines of file sharing code. (Anyone guess I am not in sales?)

But, the main point here is the open source graph layer/BSON proposal solves the problem. The client, the unregulated worker, wants to collect information to try in a proposal. The predicate is, try, the object is his proposal, the subjects are  named graphes in the enterprise semantic nest.

Simple, we encode the rules into a nested graph, which uses ontology matching. These rules are convolved first with the client proposed changes, query optimization.

We next convolve that result graph with the clients proposal and enterprise nest, launch the bot, as we say. The bot locates interested subgraphs via ontology matching. It collects the appropriate enterprise objects, convolves that rule set. The graph returned to the client is the 'diff' graph, which he can apply locally in his broswer. The diff graph is the changes in system dnest as a result of his proposal:
@(@(ProposalGraph,RuleSet),SystemNet) -> System result of the proposed new activity.

Remember, our bot carry BSON expression, the same thing use by Java script. So if the customer wants Commercially Off The Shelf, not a problem.  The shelf is right there, the enterprise nest, the bots can carry them around, like UPS trucks, and if you want Commercial, the bots will charge a penny.

So yes, we gonna solve that whole class of problem, no need for gazzillinare IPOs, just down load our software in a few months.

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